Welcome back to the fourth week of the series. In this post, we will be discussing the following libraries
- Rapids
- Keras
- Fast AI
Don't get lost seeing the title, you will understand the title once you finish reading the post 🕵🏻♂️.
Rapids
The RAPIDS suite of open-source software libraries and APIs give you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA primitives for low-level compute optimization and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
Tagline: Open GPU Data Science
Docs
Collab Notebook
Keras
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.
Tagline: Simple. Flexible. Powerful.
Docs
Fast AI
The fastai library simplifies training fast and accurate neural nets using modern best practices. It's based on research into deep learning best practices undertaken at fast.ai, including "out of the box" support for vision, text, tabular, and collab (collaborative filtering) models.
Tagline: Making neural nets uncool again
TBH my favourite library and tagline, they explained their tagline on their website
Learn
Docs
Peace ✌🏻,
Rohith Gilla
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